Download Towards Personalized Medicine in The Netherlands

Towards Personalized
Medicine in The
Netherlands
Clinical Decision Support and
Cognitive Computing in Oncology
© 2016 E-HEALTH WEEK AMSTERDAM
The Dutch Health Deal – CDSS in Oncology (June 8TH 2016)
Stimulating innovation between government and (private) partners
 Impactful innovations improving quality of life, efficiency, outcomes
 Initiated by ‘market’ entities
 Government removes bottlenecks for the parties involved
© 2016 E-HEALTH WEEK AMSTERDAM
© 2016 E-HEALTH WEEK AMSTERDAM
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Introduction speakers
Dr Nicky Hekster
IBM Netherlands
Technical Leader Healthcare & LifeSciences
Watson Ambassador
© 2016 E-HEALTH WEEK AMSTERDAM
Prof. Dr Sabine Linn
Netherlands Cancer Institute
Medical oncologist, specialized in
Breast Cancer
Prof. Dr Gerrit Meijer
Netherlands Cancer Institute
Diagnostic oncologist,
specialized in Translational
Gastrointestinal Oncology
Data is growing exponentially
44 zettabytes
It demands new approaches in both technology and strategy
Non-standardized data and numbers,
free text, speech, video, images,
pictures, …
80% unstructured data
Numbers, spreadsheets, standardized
data models (semantic models), …
We are here
20% structured data
2010
© 2016 E-HEALTH WEEK AMSTERDAM
2016
2020
Four industrial revolutions
Medicine is here!
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A new computer era is coming of age – cognitive computing
From automating the world to understanding the world
1900
1950
>2010
Tabulating systems
Programmable Era
Cognitive Era
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Definition of Cognition
The mental action of acquiring knowledge and understanding through thought,
experience, and our senses
 Knowledge
 Ability to understand
 Ideation, conviction
 Sensation, observation
 Imagination
 Store in and retrieve from memory
 Problem solving capabilities
 Think
 Language
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How cognition works
To become an expert
Observation
Interpretation
Decision
Evaluation
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How our biran wkros?
I cdn'uolt blveiee taht I cluod aulaclty uesdnatnrd waht I was rdanieg: the
phaonmneel pweor of the hmuan mnid. Aoccdrnig to a rseearch taem at
Cmabrigde Uinervtisy, it deosn't mttaer in waht oredr the ltteers in a wrod are,
the olny iprmoatnt tihng is taht the frist and lsat ltteer be in the rghit pclae. The
rset can be a taotl mses and you can sitll raed it wouthit a porbelm. Tihs is
bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a
wlohe.
© 2016 E-HEALTH WEEK AMSTERDAM
History of AI
1990s: AI on www
AI-based extraction programs
prevalent on www
2005: Autonomous car
Stanford-built autonomous car wins DARPA
Grand Challenge
1956: “Birth” of AI
John McCarthy coins term
artificial intelligence (AI) at
Dartmouth Conference
1950s
1960s
1970s
1950: Turing Test
Turing introduces way to test
for intelligent behavior
1965: First Expert System
Stanford team led by Ed
Feigenbaum creates DENDRAL and
MYCIN
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2014: Market changes
1974- 1980: 1st AI “Winter”
1980s
IBM formation of Watson Group and
Google acquisition of Nest Labs
1990s
2000s
2010…
2011:
Watson
1987- 1993: 2nd AI “Winter”
IBM’s
Watson
competes
and wins
on
Jeopardy!
1997: Deep Blue
IBM Deep Blue defeats World
Chess Champion
2016: Google
DeepMind
AlphaGo wins Go
2014: Facebook
Recognize individuals
DeepText
The Grand Challenges
Chess – Deep Blue (1997)
• A finite, mathematically well-defined search space (10120)
• Limited number of moves and states on an 8 x 8 board
• Grounded in explicit, unambiguous mathematical rules
Human Language – Watson (2011)
• Ambiguous, contextual and implicit
• Grounded only in human cognition
• Seemingly infinite number of ways to express the same meaning
Go – DeepMind (2016)
• A finite, mathematically well-defined but very large search space (10761)
• Limited number of positions and states on a 19 x 19 board
• Based on explicit, unambiguous logical rules
© 2016 E-HEALTH WEEK AMSTERDAM
Thomas J. Watson
(1874 – 1956)
© 2016 E-HEALTH WEEK AMSTERDAM
14 February 2011
© 2016 E-HEALTH WEEK AMSTERDAM
IBM Watson is based on
Big Data &
Analytics
Artificial
Intelligence
Cognitive
Experience
Cognitive
Knowledge
Computing
Infrastructure
Data Mining,
Optimization,
Text Analytics
Machine Learning,
Natural Language
Processing,
Algorithms &
Theory
HCI, Speech,
Translation,
Machine Vision,
Visualization
Knowledge
Representation,
Ontologies,
Semantics, Context
High Performance
Computing,
Distributed Systems,
Programming
Models & Tools
© 2016 E-HEALTH WEEK AMSTERDAM
Watson is an example of a cognitive system
Intelligence Amplification
1 Understands
2 Reasons, generates and
evaluates hypothesis for
better outcomes
natural
language and
human
speech
3 Adapts and
learns from
user selections
and responses
Watson does not predict! Watson only explains from a very large
set of data and helps human beings taking complex decisions.
© 2016 E-HEALTH WEEK AMSTERDAM
Brief history of IBM Watson
IBM
Research Project
Jeopardy!
Grand Challenge
(2006 – )
(Feb 2011)
Watson
for
Healthcare
Watson
for Financial
Services
(Aug 2011 –)
(Mar 2012 – )
Expansion
Commercialization
Demonstration
R&D
© 2016 E-HEALTH WEEK AMSTERDAM
Internal start-up division
Watson
Group
(Jan 2014 – )
Cross-industry
Applications
Watson
Health Group
Watson
IoT Group
(April 2015 – )
(Jan 2016 – )
Health and
Lifesciences
Applications
Internet of
Things
Applications
IBM Bluemix PaaS platform
© 2016 E-HEALTH WEEK AMSTERDAM
Catalog will grow from 28 to 50 APIs (2016)
The Watson that competed on Jeopardy! in 2011
Since then, Watson has grown to
comprised what
a family of 28 APIs.
is now a single API—Q&A—built
on five underlying technologies.
Questio
ns
&
Answers
Language
Detection
Visual
Recognition
Question
Analysis
Feature
Engineering
Ontology
Natural
Language
Classifier
Concept
Tagging
Language
Translation
Retrieve
&
Rank
Text to
Speech
Tradeoff
Analytics
Speech
to
Text
© 2016 E-HEALTH WEEK AMSTERDAM
Text
Extraction
Watson
News
Entity
Extraction
Decision
Support
Risk
Stratification
Policy
Identification
Video
Augmentation
Face
Detection
Statistic
al
Dialog
Criteria
Classification
Fusion
Q&A
Image
Tagging
Knowledg
e Studio
Service
Knowledge
Graph
Message
Resonance
Tone
Analyze
r
Easy
Adaptati
on
Emotion
Analysis
Usage
Insights
Taxonom
y
Analysis
Author
Extraction
Answer
Generation
Sentime
nt
Analysis
Dialog
Concept
Insights
Decision
Optimization
Image
Link
Extraction
Feed
Detection
Relationship
Extraction
Concept
Expansion
Natural
Language
Processing
Machine
Learning
Keyword
Extraction
Personality
Insights
By the end of 2016, there will
be nearly 50 Watson APIs—
with more added every year.
Q&A
Qualification
Knowledge
Canvas
Factoid
Pipeline
Case
Evaluation
American Cancer Society creates a Virtual Cancer Health
Advisor with IBM Watson
 The advisor will anticipate the needs of people with different types of cancers, at different
stages of disease, and at various points in treatment.
 It will become increasingly personalized as individuals engage with it, effectively getting
“smarter” each time it is used. The advisor will use ACS's cancer.org 14.000 pages of
detailed information on more than 70 cancer topics.
 ACS and IBM also envision incorporating Watson’s voice recognition and natural language
processing technology, enabling users to ask questions and receive audible responses.
© 2016 E-HEALTH WEEK AMSTERDAM
Applying cognitive tools to medical imaging will help assist
medical experts
Current diagnosis based on
imaging alone or with limited context
IBM combines multiple data sources and
cognitive capabilities to assist the physician
+
Imaging
Clinical
Records
+
Knowledge
IBM Cognitive
Capabilities
Providing evidenced based options
© 2016 E-HEALTH WEEK AMSTERDAM
Anomaly detection involves complex analytics
Reference
Raw image
Learn from databases
Annotated reference
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Highlighted anatomy
Before registration
Segmented arteries
Arterial features
After registration
Anomaly (stenosis)
Healthcare and Lifesciences professionals are suffering from
Infobesity
 Medical information doubles every 5 years. By 2020 it is expected to double every quarter.
 80% of the healthcare professionals spends at most 5 hrs/month to keep abreast of his/her domain
 80% of the information is unstructured
 Only 20% of the knowledge doctors use is evidence based: 1 out of 5 diagnoses are wrong or
incomplete.
© 2016 E-HEALTH WEEK AMSTERDAM
© 2016 E-HEALTH WEEK AMSTERDAM
Creating a Corpus of Knowledge for Cancer Care
Based on > 290 medical journals, > 200 textbooks and > 12 million pages free text
 Ingestion of NCCN guidelines for breast cancer and lung cancer
• Roughly 500,000 unique combinations of breast cancer patient attributes.
• Roughly 50,000 unique combinations of lung cancer patient attributes.
 Over 600,000 pieces of evidence ingested, from 42 different publications/publishers
• The Breast Journal, National Comprehensive Cancer Network (Clinical Practice Guidelines, Drug and Biologics
compendium, et al.), American Journal Of Hematology, Annals Of Neurology, CA: A Cancer Journal For Clinicians,
Cancer Journal, Cochrane, EBSCO, Hematological Oncology, Hepatology, International Journal Of Cancer, Journal
Of Gene Medicine, Journal of Clinical Oncology, Journal of Oncology Practice, Massachusetts Medical Society
Journal Watch, Massachusetts Medical Society New England Journal Of Medicine, Merck, Nephrology, UptoDate,
Clinical Lung Cancer, Current Problems in Cancer, Cancer Treatment Reviews, Elsevier's Monographs in Cancer
(multiple), Clinical Breast Cancer, European Journal of Cancer, Lung Cancer (the journal).
• YAGO, DBpedia, WordNet
© 2016 E-HEALTH WEEK AMSTERDAM
© 2016 E-HEALTH WEEK AMSTERDAM
Watson Cognitive in de Gezondheidszorg
Ongoing Training Partner
Watson for Oncology, trained
by Memorial Sloan Kettering
available in clinical use in lung,
breast, colon and rectal cancer
Baylor College of Medicine
Published results of use with
Watson Discovery Advisor –
identified 7 targets for P53
activation within weeks
© 2016 E-HEALTH WEEK AMSTERDAM
Bumrungrad International
Hospital
5 year agreement for Watson
for Oncology
Watson Genomics Advisor
Secured 13 Cancer and
academic medical centers for
beta testing
MD Anderson
Introduced proprietary
solution with Watson for
clinical use for Leukemia and
Molecular Targeted Therapies
Department of Veterans
Affairs
Selected Watson to analyze
EMRs in a demo project
Metropolitan
Health
Manipal Hospitals
Uses Watson
Mayo Clinic
Selected
WatsonEngagement
for Oncology
to handle
more that 12
Completed testing with Clinical Advisor
to identify
evidence-based
client
interactions
per
Trial Matching for lung, breast, million
treatment options among
year/r.
colon and rectal cancer
200.000 patients/y
Mayo Clinic
Selected Watson to analyze
EMRs for Clinical Efficiency and
Effectiveness Program
Manipal Hospitals
Selected Watson for Oncology
to identify evidence-based
treatment options among
200.000 patients per
yearpatients/year.
Sabine Linn
© 2016 E-HEALTH WEEK AMSTERDAM
Disclosures
 Sabine Linn received institutional unrestricted research grants from:
•Amgen, AstraZeneca, Genentech, Roche, Sanofi
 Sabine Linn is named inventor on a BRCAness signature patent
 Sabine Linn was an advisory board member for Novartis, Pfizer, Roche, Sanofi,
AstraZeneca
 Sabine Linn is a member (pro bono) of the scientific advisory boards of
Cergentis and Philips Health BV
© 2016 E-HEALTH WEEK AMSTERDAM
Where are we heading for?
© 2016 E-HEALTH WEEK AMSTERDAM
Begin with the end in mind (S. Covey)
© 2016 E-HEALTH WEEK AMSTERDAM
Why?
 Too much scientific knowledge to keep up with as a clinician
 Desirable to have continuous medical education during outpatient
clinics
 Standardize quality of care
 More patients in clinical trials
 Population-based datasets for research
© 2016 E-HEALTH WEEK AMSTERDAM
How?
 Case: test intelligence amplification system (IBM Watson)
 Gap analysis – translation to Dutch situation
 Assess cost-effectiveness
 Arrange governance
 Ethical, legal and social aspects
•E.g. Data ownership, intellectual property etc
© 2016 E-HEALTH WEEK AMSTERDAM
Demo IBM Watson Health for Oncology
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Added value
 Free text extraction (saves time)
 Check for completeness of diagnostic information
 More treatment options
 Suggestions for eligible studies
 Treatment overview for the patient
 Continuous medical education during outpatient clinics
© 2016Data
mining for self learning decision support system
E-HEALTH WEEK AMSTERDAM
Risks?
 Who is responsible?
 Simplifying medical complexity
 IT will never be able to capture all symptom combinations in models
 It is a SUPPORT TOOL
 Cost-effectiveness?
© 2016 E-HEALTH WEEK AMSTERDAM
Gerrit Meijer
© 2016 E-HEALTH WEEK AMSTERDAM